代表性成果
1.Zhang Zhentao,Wang Jinru. Perturbation Upper Bounds for Singular Subspaces with a Kind of Heteroskedastic Noise and Its Application in Clustering. Math. Meth. Appl. Sci., 2022, 1-16.
2.Xinyu Qi,Jinru Wang,Jiating Shao.Minimax perturbation bounds of the low-rank matrix under Ky Fan norm，AIMS Mathematics, 2022，7(5): 7595–7605.
3.Jinru Wang, Wenhui Shi, Xiaochen Zeng. Optimal wavelet estimators of the heteroscedastic pointspread effects and Gauss white noises model, Communications in Statistics - Theory and Methods, 2022, 51(5): 1133-1154.
4.Cong Wu,Jinru Wang, Xiaochen Zeng. Adaptive and Optimal Point-wise Estimations for Densities in GARCH-type Model by Wavelets，Journal of Computational Mathematics, 2022, 40(1):108–126.
5.Kaikai Cao,Jinru Wang, Xiaochen Zeng. Wavelet estimations for mixed densities under multiplicative censoring. Mathematical Methods in the Applied Sciences, 2020, 43(2): 808-821.
6.Jinru Wang, Zhenming Zhang, Xue Zhang, Xiaochen Zeng. Wavelet pointwise estimations under multiplicative censoring. International Journal of Wavelets, Multiresolution and Information Processing, 2020, 18(4), 2050020
7.Jinru Wang,WeiLiu.Wavelet estimations for heteroscedastic super smooth errors.Communications in Statistics- Theory and Methods,2019, 48(10): 2356-2371.
8.Zeng, Xiaochen;Wang, Jinru.Wavelet density deconvolution estimations with heteroscedastic measurement errors. Statistics and Probability Letters，2018,134:79–85.
9.Lin Hu, Xiaochen Zeng,JinruWang. Wavelet optimal estimations for a two-dimensional continuous-discrete density function over Lp risk. Journal of Inequalities and Applications. 2018, 279，https://doi.org/10.1186/s13660-018-1868-7
10.Guo Huijun;Wang Jinru;Tian, Xinyan.The mean consistency of wavelet estimators for convolutions of the density functions.J. Comput. Appl. Math.2018,343:1–11.
11.Jinru Wang，QingqingZhang，Junke Kou.Wavelet estimators for the derivatives of the density function from data contaminated with heteroscedastic measurement errors. Communications in Statistics: Theory and Methods.2017, 46(15):7337–7354.
